

import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()



class        A__(a_,  a_,  a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				Optional[int]       					=			StableUnCLIPPipeline
						_A    :				List[Any]       					=			TEXT_TO_IMAGE_PARAMS
						_A    :				Any       					=			TEXT_TO_IMAGE_BATCH_PARAMS
						_A    :				Optional[Any]       					=			TEXT_TO_IMAGE_IMAGE_PARAMS
						_A    :				str       					=			TEXT_TO_IMAGE_IMAGE_PARAMS

						# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
						_A    :				Optional[int]       					=			False





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									a_						:						Dict 							= 32
									a_						:						Optional[int] 							= embedder_hidden_size

									# prior components

									torch.manual_seed(0    )
									a_						:						Optional[Any] 							= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

									torch.manual_seed(0    )
									a_						:						str 							= CLIPTextModelWithProjection(
									    CLIPTextConfig(
									        bos_token_id=0  ,      eos_token_id=2  ,      hidden_size=_lowercase  ,      projection_dim=_lowercase  ,      intermediate_size=37  ,      layer_norm_eps=1e-05  ,      num_attention_heads=4  ,      num_hidden_layers=5  ,      pad_token_id=1  ,      vocab_size=1_000  ,      )    )

									torch.manual_seed(0    )
									a_						:						Dict 							= PriorTransformer(
									    num_attention_heads=2  ,      attention_head_dim=12  ,      embedding_dim=_lowercase  ,      num_layers=1  ,      )

									torch.manual_seed(0    )
									a_						:						Any 							= DDPMScheduler(
									    variance_type="""fixed_small_log"""  ,      prediction_type="""sample"""  ,      num_train_timesteps=1_000  ,      clip_sample=_lowercase  ,      clip_sample_range=5.0  ,      beta_schedule="""squaredcos_cap_v2"""  ,      )

									# regular denoising components

									torch.manual_seed(0    )
									a_						:						Any 							= StableUnCLIPImageNormalizer(embedding_dim=_lowercase    )
									a_						:						Tuple 							= DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""    )

									torch.manual_seed(0    )
									a_						:						Optional[Any] 							= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

									torch.manual_seed(0    )
									a_						:						Optional[int] 							= CLIPTextModel(
									    CLIPTextConfig(
									        bos_token_id=0  ,      eos_token_id=2  ,      hidden_size=_lowercase  ,      projection_dim=32  ,      intermediate_size=37  ,      layer_norm_eps=1e-05  ,      num_attention_heads=4  ,      num_hidden_layers=5  ,      pad_token_id=1  ,      vocab_size=1_000  ,      )    )

									torch.manual_seed(0    )
									a_						:						Optional[Any] 							= UNetaDConditionModel(
									    sample_size=32  ,      in_channels=4  ,      out_channels=4  ,      down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")  ,      up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")  ,      block_out_channels=(32, 64)  ,      attention_head_dim=(2, 4)  ,      class_embed_type="""projection"""  ,      projection_class_embeddings_input_dim=embedder_projection_dim * 2  ,      cross_attention_dim=_lowercase  ,      layers_per_block=1  ,      upcast_attention=_lowercase  ,      use_linear_projection=_lowercase  ,      )

									torch.manual_seed(0    )
									a_						:						Any 							= DDIMScheduler(
									    beta_schedule="""scaled_linear"""  ,      beta_start=0.0_0_0_8_5  ,      beta_end=0.0_1_2  ,      prediction_type="""v_prediction"""  ,      set_alpha_to_one=_lowercase  ,      steps_offset=1  ,      )

									torch.manual_seed(0    )
									a_						:						List[str] 							= AutoencoderKL()

									a_						:						List[Any] 							= {
									    # prior components
									    """prior_tokenizer""": prior_tokenizer,
									    """prior_text_encoder""": prior_text_encoder,
									    """prior""": prior,
									    """prior_scheduler""": prior_scheduler,
									    # image noising components
									    """image_normalizer""": image_normalizer,
									    """image_noising_scheduler""": image_noising_scheduler,
									    # regular denoising components
									    """tokenizer""": tokenizer,
									    """text_encoder""": text_encoder,
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									}

									return components





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase=0    )     -> Optional[Any]:
									if str(_lowercase    ).startswith("""mps"""    ):
												a_						:						Optional[int] 							= torch.manual_seed(_lowercase    )
									else:
												a_						:						Dict 							= torch.Generator(device=_lowercase    ).manual_seed(_lowercase    )
									a_						:						List[Any] 							= {
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """prior_num_inference_steps""": 2,
									    """output_type""": """numpy""",
									}
									return inputs





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Optional[int] 							= torch_device == """cpu"""

									self._test_attention_slicing_forward_pass(test_max_difference=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						Any 							= torch_device in ["""cpu""", """mps"""]

									self._test_inference_batch_single_identical(test_max_difference=_lowercase    )







@slow
@require_torch_gpu
class        A__(unittest.TestCase							):
						"""simple docstring"""






						def 			UpperCamelCase__					(    self    )     -> Dict:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Tuple 							= load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""    )

									a_						:						Tuple 							= StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""  ,      torch_dtype=torch.floataa    )
									pipe.to(_lowercase    )
									pipe.set_progress_bar_config(disable=_lowercase    )
									# stable unclip will oom when integration tests are run on a V100,
									# so turn on memory savings
									pipe.enable_attention_slicing()
									pipe.enable_sequential_cpu_offload()

									a_						:						Optional[int] 							= torch.Generator(device="""cpu"""    ).manual_seed(0    )
									a_						:						Dict 							= pipe("""anime turle"""  ,      generator=_lowercase  ,      output_type="""np"""    )

									a_						:						Optional[Any] 							= output.images[0]

									assert image.shape == (768, 768, 3)

									assert_mean_pixel_difference(_lowercase  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									a_						:						Union[str, Any] 							= StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""  ,      torch_dtype=torch.floataa    )
									a_						:						Optional[Any] 							= pipe.to(_lowercase    )
									pipe.set_progress_bar_config(disable=_lowercase    )
									pipe.enable_attention_slicing()
									pipe.enable_sequential_cpu_offload()

									a_						:						Optional[int] 							= pipe(
									    """anime turtle"""  ,      prior_num_inference_steps=2  ,      num_inference_steps=2  ,      output_type="""np"""  ,      )

									a_						:						Optional[int] 							= torch.cuda.max_memory_allocated()
									# make sure that less than 7 GB is allocated
									assert mem_bytes < 7 * 10**9





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[Any] 							= [
			    """encoder.version""",
			    """decoder.version""",
			    """model.encoder.version""",
			    """model.decoder.version""",
			    """decoder.output_projection.weight""",
			    """_float_tensor""",
			    """encoder.embed_positions._float_tensor""",
			    """decoder.embed_positions._float_tensor""",
			]
			for k in ignore_keys:
						state_dict.pop(a__  ,	a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_							,					a_						:						List[Any] 							= emb.weight.shape
			a_						:						Dict 							= nn.Linear(a__  ,	a__  ,	bias=a__)
			a_						:						str 							= emb.weight.data
			return lin_layer


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= torch.load(a__  ,	map_location="""cpu""")
			a_						:						int 							= mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
			a_						:						Dict 							= mam_aaa["""model"""]
			remove_ignore_keys_(a__)
			a_						:						List[str] 							= state_dict["""encoder.embed_tokens.weight"""].shape[0]

			a_						:						Union[str, Any] 							= MaMaaaConfig(
			    vocab_size=a__  ,	max_position_embeddings=1_0_2_4  ,	encoder_layers=args.encoder_layers  ,	decoder_layers=args.decoder_layers  ,	encoder_attention_heads=args.encoder_attention_heads  ,	decoder_attention_heads=args.decoder_attention_heads  ,	encoder_ffn_dim=args.encoder_ffn_embed_dim  ,	decoder_ffn_dim=args.decoder_ffn_embed_dim  ,	d_model=args.encoder_embed_dim  ,	encoder_layerdrop=args.encoder_layerdrop  ,	decoder_layerdrop=args.decoder_layerdrop  ,	dropout=args.dropout  ,	attention_dropout=args.attention_dropout  ,	activation_dropout=args.activation_dropout  ,	activation_function="""relu"""  ,	)

			a_						:						Union[str, Any] 							= state_dict["""decoder.embed_tokens.weight"""]
			a_						:						List[Any] 							= MaMaaaForConditionalGeneration(a__)
			model.model.load_state_dict(a__  ,	strict=a__)
			a_						:						Any 							= make_linear_from_emb(model.model.shared)

			return model


if __name__ == "__main__":
	__snake_case				:      Union[str, Any]   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
	parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	__snake_case				:      int   		=			parser.parse_args()
	__snake_case				:      Any   		=			convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
	model.save_pretrained(args.pytorch_dump_folder_path)


